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Dataset Title:  Haloptilus\u00a0longicornis\u00a0population structure (Atlantic Ocean) -
Microsatellite data. Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_699458)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  station {
    String bcodmo_name "station";
    String description "Station number where sampling occurred";
    String long_name "Station";
    String units "unitless";
  }
  sample_id {
    String bcodmo_name "sample";
    String description "PI issued sample ID number";
    String long_name "Sample Id";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  diploidGenotype1_HALOMS175 {
    Byte _FillValue 127;
    Byte actual_range 93, 113;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOMS175";
    String units "count";
  }
  diploidGenotype2_HALOMS175 {
    Byte _FillValue 127;
    Byte actual_range 93, 119;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOMS175";
    String units "count";
  }
  diploidGenotype1_HALOMS27 {
    Int16 _FillValue 32767;
    Int16 actual_range 214, 258;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOMS27";
    String units "count";
  }
  diploidGenotype2_HALOMS27 {
    Int16 _FillValue 32767;
    Int16 actual_range 220, 258;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOMS27";
    String units "count";
  }
  diploidGenotype1_HALOMS32 {
    Int16 _FillValue 32767;
    Int16 actual_range 126, 150;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOMS32";
    String units "count";
  }
  diploidGenotype2_HALOMS32 {
    Int16 _FillValue 32767;
    Int16 actual_range 126, 159;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOMS32";
    String units "count";
  }
  diploidGenotype1_HALOMS86 {
    Int16 _FillValue 32767;
    Int16 actual_range 136, 181;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOMS86";
    String units "count";
  }
  diploidGenotype2_HALOMS86 {
    Int16 _FillValue 32767;
    Int16 actual_range 136, 181;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOMS86";
    String units "count";
  }
  diploidGenotype1_HALOM264 {
    Int16 _FillValue 32767;
    Int16 actual_range 151, 172;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOM264";
    String units "count";
  }
  diploidGenotype2_HALOM264 {
    Int16 _FillValue 32767;
    Int16 actual_range 163, 177;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOM264";
    String units "count";
  }
  diploidGenotype1_HALOMS91 {
    Int16 _FillValue 32767;
    Int16 actual_range 190, 212;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOMS91";
    String units "count";
  }
  diploidGenotype2_HALOMS91 {
    Int16 _FillValue 32767;
    Int16 actual_range 194, 218;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOMS91";
    String units "count";
  }
  diploidGenotype1_HALOMX66 {
    Int16 _FillValue 32767;
    Int16 actual_range 178, 193;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype1 HALOMX66";
    String units "count";
  }
  diploidGenotype2_HALOMX66 {
    Int16 _FillValue 32767;
    Int16 actual_range 181, 196;
    String bcodmo_name "count";
    String description "Diploid genotypes reported for each locus and individual";
    String long_name "Diploid Genotype2 HALOMX66";
    String units "count";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Refer to the following publication for complete methodology details:
 
Goetze, E.,\\u00a0Andrews, K., Peijnenburg, K. T. C. A., Portner, E., Norton,
E. L. (2015) Temporal Stability of Genetic Structure in a Mesopelagic
Copepod.\\u00a0\\u00a0PLoS One\\u00a010(8):
e0136087.\\u00a0[doi:10.1371/journal.pone.0136087](\\\\\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136087\\\\\")
 
In summary (excerpted from above):
 
For\\u00a0H.\\u00a0longicornis species 1, deviations from Hardy-Weinberg
equilibrium (HWE) and linkage disequilibrium were examined using ARLEQUIN
v3.5.1.3 and GENEPOP v4.2 for all microsatellite loci [36\\u201338]. We tested
for the presence of null alleles in microsatellite data using MICROCHECKER
v2.2.3 [39], and estimated null allele frequencies and calculated population
pairwise\\u00a0FST\\u00a0values with correction for null alleles in FreeNA [40].
Microsatellite genetic diversity indices of observed and expected
heterozygosity, average alleles per locus, and allele richness were calculated
in GENETIX v4.05 and FSTAT [35,41]. Pairwise\\u00a0FST\\u00a0values were
calculated among all sample sites using both microsatellite and mtCOII data,
as a measure of population subdivision across samples (ARLEQUIN v3.5.1.3,
[38]). Significance was assessed following correction for multiple comparisons
using the false discovery rate (FDR, [42,43]). Pairwise \\u03a6ST\\u00a0values
also were calculated for the mtCOII data. We identified the nucleotide
substitution model that best fit our mtCOII data using the Akaike Information
Criterion, as implemented in jModelTest v2.1.4 [44], and the K81 or three-
parameter model was selected as the best model (TPM3uf+G). The Tamura and Nei
substitution model, which was the closest available model in Arlequin, was
used to calculate pairwise and global \\u03a6ST\\u00a0values, and to estimate
genetic diversity at each site. Hierarchical Analyses of Molecular Variance
(AMOVA) based on\\u00a0FST\\u00a0were carried out to partition the genetic
variance across both space (ocean gyres) and time (sampling years), for both
marker types. In these analyses, we tested for population structure under the
following groupings: with samples stratified by (1) northern and southern
subtropical gyres (2 gyres), and (2) across two sampling years (2010, 2012).
Global\\u00a0FST\\u00a0values were estimated using non-hierarchical AMOVAs among
all samples, as well as among subsets of the data across ocean gyres and
sampling years. Significance was tested with 10,000 permutations of genotypes
or haplotypes among populations. Principal coordinate analysis (PCA) plots of
linearized pairwise\\u00a0FST\\u00a0values based on both mtCOII and
microsatellite data were used to visualize spatial and temporal genetic
differentiation among samples. Population structure was further examined using
a Bayesian clustering method implemented in STRUCTURE [45,46] for
microsatellite loci. We used admixture and correlated allele frequency models,
with a burn-in of 105\\u00a0steps followed by 106\\u00a0steps, with and without
using sampling location as a prior. We ran these analyses for each of the 2010
and 2012 datasets using\\u00a0K\\u00a0= 1 to\\u00a0K\\u00a0= 10, and for the
dataset of combined years using\\u00a0K\\u00a0= 1 to\\u00a0K\\u00a0= 20. We ran
three separate replicates for each K to investigate consistency of Pr(X|K).
The true\\u00a0K\\u00a0was evaluated by visual inspection of barplots and
comparing Pr(X|K) across\\u00a0K\\u00a0values.";
    String awards_0_award_nid "537990";
    String awards_0_award_number "OCE-1338959";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1338959";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "539716";
    String awards_1_award_number "OCE-1029478";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1029478";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "David L. Garrison";
    String awards_1_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Haloptilus longicorns population structure 
  Erica Goetze, PI 
  Version 20 March 2017";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2017-05-04T17:20:16Z";
    String date_modified "2019-03-28T19:36:57Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.699458.1";
    String history 
"2024-04-19T13:54:18Z (local files)
2024-04-19T13:54:18Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_699458.das";
    String infoUrl "https://www.bco-dmo.org/dataset/699458";
    String institution "BCO-DMO";
    String instruments_0_acronym "Thermal Cycler";
    String instruments_0_dataset_instrument_description "PCR products were genotyped";
    String instruments_0_dataset_instrument_nid "699475";
    String instruments_0_description 
"General term for a laboratory apparatus commonly used for performing polymerase chain reaction (PCR). The device has a thermal block with holes where tubes with the PCR reaction mixtures can be inserted. The cycler then raises and lowers the temperature of the block in discrete, pre-programmed steps.

(adapted from http://serc.carleton.edu/microbelife/research_methods/genomics/pcr.html)";
    String instruments_0_instrument_name "PCR Thermal Cycler";
    String instruments_0_instrument_nid "471582";
    String instruments_0_supplied_name "ABI3730 Genetic Analyzer";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, diploid, diploidGenotype1_HALOM264, diploidGenotype1_HALOMS175, diploidGenotype1_HALOMS27, diploidGenotype1_HALOMS32, diploidGenotype1_HALOMS86, diploidGenotype1_HALOMS91, diploidGenotype1_HALOMX66, diploidGenotype2_HALOM264, diploidGenotype2_HALOMS175, diploidGenotype2_HALOMS27, diploidGenotype2_HALOMS32, diploidGenotype2_HALOMS86, diploidGenotype2_HALOMS91, diploidGenotype2_HALOMX66, dmo, erddap, genotype1, genotype2, halom264, haloms175, haloms27, haloms32, haloms86, haloms91, halomx66, management, oceanography, office, preliminary, sample, sample_id, station";
    String license "https://www.bco-dmo.org/dataset/699458/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/699458";
    String param_mapping "{'699458': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/699458/parameters";
    String people_0_affiliation "University of Hawaii at Manoa";
    String people_0_affiliation_acronym "SOEST";
    String people_0_person_name "Erica Goetze";
    String people_0_person_nid "473048";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Hannah Ake";
    String people_1_person_nid "650173";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Plankton Population Genetics,Plankton_PopStructure";
    String projects_0_acronym "Plankton Population Genetics";
    String projects_0_description 
"Description from NSF award abstract:
Marine zooplankton show strong ecological responses to climate change, but little is known about their capacity for evolutionary response. Many authors have assumed that the evolutionary potential of zooplankton is limited. However, recent studies provide circumstantial evidence for the idea that selection is a dominant evolutionary force acting on these species, and that genetic isolation can be achieved at regional spatial scales in pelagic habitats. This RAPID project will take advantage of a unique opportunity for basin-scale transect sampling through participation in the Atlantic Meridional Transect (AMT) cruise in 2014. The cruise will traverse more than 90 degrees of latitude in the Atlantic Ocean and include boreal-temperate, subtropical and tropical waters. Zooplankton samples will be collected along the transect, and mitochondrial and microsatellite markers will be used to identify the geographic location of strong genetic breaks within three copepod species. Bayesian and coalescent analytical techniques will test if these regions act as dispersal barriers. The physiological condition of animals collected in distinct ocean habitats will be assessed by measurements of egg production (at sea) as well as body size (condition index), dry weight, and carbon and nitrogen content. The PI will test the prediction that ocean regions that serve as dispersal barriers for marine holoplankton are areas of poor-quality habitat for the target species, and that this is a dominant mechanism driving population genetic structure in oceanic zooplankton.
Note: This project is funded by an NSF RAPID award. This RAPID grant supported the shiptime costs, and all the sampling reported in the AMT24 zooplankton ecology cruise report (PDF).
Online science outreach blog at: https://atlanticplankton.wordpress.com";
    String projects_0_end_date "2015-11";
    String projects_0_geolocation "Atlantic Ocean, 46 N - 46 S";
    String projects_0_name "Basin-scale genetics of marine zooplankton";
    String projects_0_project_nid "537991";
    String projects_0_start_date "2013-12";
    String projects_1_acronym "Plankton_PopStructure";
    String projects_1_description 
"Description from NSF award abstract:
This research will test whether habitat depth specialization is a primary trait driving large-scale population genetic structure in open ocean zooplankton species. Very little is known about population connectivity in marine zooplankton. Although zooplankton were long thought to be high-gene-flow systems with little genetic differentiation among populations, recent observations have challenged this view. In fact, zooplankton species may be genetically subdivided at macrogeographic, regional, or even smaller spatial scales. Recent studies also indicate that subtle, species-specific ecological factors play an important role in controlling gene flow among plankton populations. The investigator hypothesizes that depth-related habitat, including diel vertical migration (DVM) behavior, plays a critical role in controlling dispersal of plankton among ocean regions, through interactions with ocean circulation and bathymetry. This study will compare the population genetic structures of eight planktonic copepods that utilize different depth-related habitats, in order to test key predictions of genetic structure based on the interaction of organismal depth with the oceanographic environment. The objectives of the research are to:
1) Develop novel nuclear markers that can be used to resolve genetic structure and estimate gene flow among copepod populations,
2) Characterize the spatial patterns of gene flow among populations in distinct ocean regions of the Atlantic, Pacific, and Indian Oceans for eight target species using a multilocus approach, and
3) Test the central hypothesis that depth-related habitat will significantly impact the extent of genetic structure both across and within ocean basins, the magnitude and direction of gene flow among populations, and in the timing of major slitting events within species.
Drawing on genomic resources (cDNA libraries) recently developed by the PI, five (or more) polymorphic nuclear markers will be developed for each species. These new markers will be used, in combination with the mitochondrial gene cytochrome oxidase I, to characterize the population genetic structure of each species throughout its global distribution using graph theoretic and coalescent analytical techniques. Gene flow among populations and the timing of major splitting events will be estimated under a coalescent model (IMa), and empirical support for the hypothesis of depth-related trends in population structure will be assessed using graph theoretic congruence tests. Because the depth specialization and diel vertical migration behaviors of the target species are representative of distinct zooplankton species groups, the results of this study will have broad implications for understanding and predicting the genetic structure of these important grazers in pelagic ecosystems.
Publications produced with support from this award include:
Burridge, A., Goetze, E., Raes, N., Huisman, J., Peijnenburg, K. T. C. A.  (in revision)  Global biogeography and evolution of Cuvierina pteropods.   BMC Evolutionary Biology.
Andrews, K. R., Norton, E. L., Fernandez-Silva, I., Portner†, E. Goetze, E. (in press) Multilocus evidence for globally-distributed cryptic species and distinct populations across ocean gyres in a mesopelagic copepod.  Molecular Ecology.
Halbert , K. M. K., Goetze, E., Carlon, D. B. (2013) High cryptic diversity across the global range of the migratory planktonic copepods Pleuromamma piseki and P. gracilis.  PLOS One 8(10): e77011. doi:10.1371/journal.pone.0077011
Norton , E. L., Goetze, E. (2013) Equatorial dispersal barriers and limited connectivity among oceans in a planktonic copepod.  Limnology and Oceanography 58: 1581-1596.
Peijnenburg, K. T. C. A., Goetze, E. (2013) High evolutionary potential of marine zooplankton.  Ecology & Evolution 3(8): 2765-2781.  doi: 10.1002/ece3.644   (both authors contributed equally).
Fernandez-Silva, I., Whitney, J., Wainwright, B., Andrews, K. R., Ylitalo-Ward, H., Bowen, B. W., Toonen, R. J., Goetze, E., Karl, S. A. (2013) Microsatellites for Next-Generation Ecologists: A Post-Sequencing Bioinformatics Pipeline.  PLOS One 8(2): e55990. doi:10.1371/journal.pone.0055990
Bron, J. E., Frisch, D., Goetze, E., Johnson, S. C., Lee, C. E., Wyngaard, G. A. (2011) Observing Copepods through a Genomic Lens.  Frontiers in Zoology 8: 22.
Goetze, E. (2011) Population differentiation in the open sea: Insights from the pelagic copepod Pleuromamma xiphias.  Integrative and Comparative Biology 51: 580-597.  
Master’s theses supported under this award include:
Emily L. Norton. Empirical and biophysical modeling studies of dispersal barriers for marine plankton. (2013).  University of Hawaii at Manoa.
K. M. K. Halbert. Genetic isolation in the open sea: Cryptic diversity in the Pleuromamma piseki - P. gracilis species complex. (2013).  University of Hawaii at Manoa.";
    String projects_1_end_date "2014-07";
    String projects_1_geolocation "Global Ocean";
    String projects_1_name "Does habitat specialization drive population genetic structure of oceanic zooplankton?";
    String projects_1_project_nid "539717";
    String projects_1_start_date "2010-08";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Haloptilus longicornis population structure (Atlantic Ocean) - Microsatellite data.";
    String title "Haloptilus\\u00a0longicornis\\u00a0population structure (Atlantic Ocean) - Microsatellite data.";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
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